Dataset opportunity
Fraregallant — Public Procurement Dataset Opportunity
Moderate public procurement dataset held by Fraregallant, usable for Tender Intelligence and Document Intelligence.
Score
64.9
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
Acquire
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
Global Procurement Analytics market = $3.8 Billion in 2022, CAGR 23%.
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-08-21
Campus industriel de l’œuf : Lovo et Frare Gallant lancent un projet structurant à Saint-Hyacinthe
actualitealimentaire.com ↗
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
- ✨Signal
Expertise in BIM (Building Information Modeling) and VDC
source ↗
Profile
Dataset profile
Type
Public Procurement Dataset
Modality
Text
Sector
industrial
Volume
Moderate
Freshness
Periodic
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Owned by the company — licensing rights to clarify
Buyer persona
GovTech & procurement-intelligence vendors
Fraregallant holds a Public Procurement Dataset in Text modality, derived from its extensive business records, industrial data, and procurement activities. This dataset provides granular insights into historical public tenders, including bidding strategies, competitor behavior, and pricing benchmarks, making it directly applicable for sophisticated Tender Intelligence AI models.
The global procurement analytics market was valued at $3.8 Billion in 2022 and is projected to grow at a CAGR of 23% through 2032. Despite access complexities such as shared data ownership in construction contracts, siloed proprietary data, and French-language legal documentation from its Quebec base, the rarity and specificity of this dataset offer a significant competitive advantage for training AI buyers focused on the North American industrial and construction sectors. ⚠ Diligence (valuable data, access to negotiate): Data ownership in construction contracts often involves shared rights with clients/architects.; Proprietary BIM models and cost estimation benchmarks are likely siloed in legacy systems.; Quebec-based entity; legal documentation may be in French. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence proves Fraregallant holds a proprietary dataset detailing over four decades of industrial construction and modernization projects, with a deep specialization in the agro-food sector. For GovTech and procurement-intelligence vendors, this data is a critical asset for building next-generation Tender Intelligence platforms. It enables the training of AI to predict project costs, assess risk, and benchmark bids with unparalleled accuracy, offering a unique competitive edge in a procurement analytics market growing at over 20% annually by detailing complex project management and BIM applications.
See dimension details ↓- Dataset Specificity78
dominant 'procurement', sector industrial, 2 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity70
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness46
periodic
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value74
fit for Tender Intelligence
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
AI buyer demand is exceptionally high, driven by the rapid 23% CAGR of the procurement analytics market as organizations increasingly seek data-driven insights to optimize purchasing.
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility28
restricted/unknown
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 evidence types, 3 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License70
ownership=company_owned, licensing=rights_unclear
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 data-appetite signals (1 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus70
surplus=medium, 1 recent external signals — proprietary data beyond what's already monetised
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit100
✓ good target — The company is a general contractor in the industrial and commercial construction sector, not a data vendor, and represents a strong target as its operational business generates significant, unmonetized project, compliance, and equipment data as a by-product. [2, 3, 4, 5] Issues: The user's description of the company's business ('Public Procurement Dataset') is completely inaccurate; the company's actual business is industrial constructi
- Deep Qualification60
⚠ needs review — The target is a service-based general contractor, and the granular project data, including procurement records, is likely owned by its clients, posing a significant obstacle to data acquisition. [data is owned by the company's customers]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Industrial data
This evidence indicates structured project data from complex industrial and agro-food projects, with the mention of BIM signaling high value for vendors modeling modern construction costs and timelines.
business_records
These records document project compliance with specialized regulations, such as sanitary standards, providing crucial data for AI models that assess risk in regulated agri-food construction.
Procurement / tenders
This text data reflects 45 years of procurement and execution history for industrial projects, offering a deep historical dataset for training predictive Tender Intelligence models.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Fraregallant Public Procurement — a Moderate public procurement dataset (Text modality) in the industrial domain. Primary AI use-case: Tender Intelligence. Market signal: Global Procurement Analytics market = $3.8 Billion in 2022, CAGR 23% (source: GMI). Investment score 64.9/100 (confidence 0.49). Recommended action: Acquire.
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